01 read Aruco

start of the new tool chain

.gitignore

@@ -4,3 +4,6 @@ dist/
 *.log
 logs/
 public/snapshots/
+
+# Aruco detection results
+test/data/screenShots/*.json

programs/01_read_ArUco_jpg_to_json.py

@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import os
+import hashlib
+import time
+from typing import Dict, Any
+
+import cv2
+import numpy as np
+
+
+# ------------------------------------------------------------
+# Utilities
+# ------------------------------------------------------------
+
+def load_intrinsics_npz(npz_path: str):
+    data = np.load(npz_path)
+
+    for k in ('camera_matrix', 'mtx', 'K'):
+        if k in data:
+            K = data[k].astype(np.float32)
+            break
+    else:
+        raise KeyError('Camera matrix not found in npz')
+
+    for k in ('dist_coeffs', 'dist', 'D'):
+        if k in data:
+            D = data[k].astype(np.float32).reshape(-1, 1)
+            break
+    else:
+        D = np.zeros((5,1), dtype=np.float32)
+
+    return K, D
+
+
+# ------------------------------------------------------------
+
+def get_aruco_detector(dict_name: str):
+    mapping = {
+        'DICT_4X4_250': cv2.aruco.DICT_4X4_250,
+        'DICT_5X5_100': cv2.aruco.DICT_5X5_100,
+        'DICT_6X6_250': cv2.aruco.DICT_6X6_250,
+        'DICT_ARUCO_ORIGINAL': cv2.aruco.DICT_ARUCO_ORIGINAL,
+    }
+
+    dict_id = mapping.get(dict_name, cv2.aruco.DICT_4X4_250)
+
+    dictionary = cv2.aruco.getPredefinedDictionary(dict_id)
+
+    try:
+        params = cv2.aruco.DetectorParameters()
+    except Exception:
+        params = cv2.aruco.DetectorParameters_create()
+
+    try:
+        detector = cv2.aruco.ArucoDetector(dictionary, params)
+        return detector, None
+    except Exception:
+        return None, (dictionary, params)
+
+
+# ------------------------------------------------------------
+
+def detect_markers(image, detector_tuple):
+    detector, fallback = detector_tuple
+
+    if detector is not None:
+        corners, ids, rejected = detector.detectMarkers(image)
+    else:
+        dictionary, params = fallback
+        corners, ids, rejected = cv2.aruco.detectMarkers(
+            image,
+            dictionary,
+            parameters=params
+        )
+
+    return corners, ids, rejected
+
+
+# ------------------------------------------------------------
+
+def compute_sharpness(gray_roi):
+    if gray_roi.size == 0:
+        return 0.0
+
+    return float(cv2.Laplacian(gray_roi, cv2.CV_64F).var())
+
+
+# ------------------------------------------------------------
+
+def compute_local_stats(gray_roi):
+    if gray_roi.size == 0:
+        return 0.0, 0.0
+
+    mean = float(np.mean(gray_roi))
+    std = float(np.std(gray_roi))
+
+    return mean, std
+
+
+# ------------------------------------------------------------
+
+def hash_file(path):
+    sha = hashlib.sha256()
+
+    with open(path, 'rb') as f:
+        while True:
+            chunk = f.read(1024 * 1024)
+            if not chunk:
+                break
+            sha.update(chunk)
+
+    return sha.hexdigest()
+
+
+# ------------------------------------------------------------
+
+def main():
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('-i', '--image', required=True)
+    parser.add_argument('-npz', '--intrinsics', required=True)
+    parser.add_argument('-cameraId', '--cameraId', required=True)
+
+    parser.add_argument(
+        '--dict',
+        default='DICT_4X4_250'
+    )
+
+    parser.add_argument(
+        '-o',
+        '--output',
+        default=None
+    )
+
+    args = parser.parse_args()
+
+    image = cv2.imread(args.image)
+
+    if image is None:
+        raise RuntimeError(f'Cannot read image: {args.image}')
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    h, w = gray.shape[:2]
+
+    K, D = load_intrinsics_npz(args.intrinsics)
+
+    detector_tuple = get_aruco_detector(args.dict)
+
+    corners_list, ids, rejected = detect_markers(gray, detector_tuple)
+
+    observations = []
+
+    if ids is not None:
+
+        ids = ids.flatten().tolist()
+
+        for i, marker_id in enumerate(ids):
+
+            corners = corners_list[i].reshape((4,2)).astype(np.float32)
+
+            center = corners.mean(axis=0)
+
+            area_px = float(cv2.contourArea(corners))
+
+            perimeter_px = float(cv2.arcLength(corners, True))
+
+            x_min = float(np.min(corners[:,0]))
+            x_max = float(np.max(corners[:,0]))
+            y_min = float(np.min(corners[:,1]))
+            y_max = float(np.max(corners[:,1]))
+
+            bbox_x = int(max(0, np.floor(x_min)))
+            bbox_y = int(max(0, np.floor(y_min)))
+            bbox_w = int(min(w - bbox_x, np.ceil(x_max - x_min)))
+            bbox_h = int(min(h - bbox_y, np.ceil(y_max - y_min)))
+
+            roi = gray[
+                bbox_y:bbox_y+bbox_h,
+                bbox_x:bbox_x+bbox_w
+            ]
+
+            sharpness = compute_sharpness(roi)
+
+            mean_gray, std_gray = compute_local_stats(roi)
+
+            edge_lengths = []
+
+            for k in range(4):
+                p1 = corners[k]
+                p2 = corners[(k+1)%4]
+                edge_lengths.append(float(np.linalg.norm(p1 - p2)))
+
+            edge_ratio = (
+                max(edge_lengths) / max(1e-6, min(edge_lengths))
+            )
+
+            obs = {
+                'marker_id': int(marker_id),
+
+                'corners_px': corners.tolist(),
+
+                'center_px': center.tolist(),
+
+                'bbox_px': {
+                    'x': x_min,
+                    'y': y_min,
+                    'w': x_max - x_min,
+                    'h': y_max - y_min
+                },
+
+                'quality': {
+                    'area_px': area_px,
+                    'perimeter_px': perimeter_px,
+                    'sharpness_laplacian': sharpness,
+                    'mean_gray': mean_gray,
+                    'std_gray': std_gray,
+                    'edge_ratio': edge_ratio,
+                    'edge_lengths_px': edge_lengths
+                }
+            }
+
+            observations.append(obs)
+
+    output = {
+        'schema_version': '1.0',
+
+        'created_utc': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()),
+
+        'camera': {
+            'camera_id': args.cameraId,
+            'intrinsics_file': os.path.abspath(args.intrinsics),
+            'camera_matrix': K.tolist(),
+            'distortion_coefficients': D.reshape(-1).tolist()
+        },
+
+        'image': {
+            'image_file': os.path.abspath(args.image),
+            'image_sha256': hash_file(args.image),
+            'width_px': int(w),
+            'height_px': int(h)
+        },
+
+        'aruco': {
+            'dictionary': args.dict,
+            'num_detected_markers': len(observations),
+            'num_rejected_candidates': int(len(rejected))
+        },
+
+        'observations': observations
+    }
+
+    if args.output is None:
+        base = os.path.splitext(args.image)[0]
+        out_json = f'{base}_aruco_detection.json'
+    else:
+        out_json = args.output
+
+    with open(out_json, 'w', encoding='utf-8') as f:
+        json.dump(output, f, indent=2)
+
+    print(f'Saved: {out_json}')
+
+
+# ------------------------------------------------------------
+
+if __name__ == '__main__':
+    main()

test/01_read_ArUco.test.js

@@ -0,0 +1,57 @@
+// test/aruco.test.js
+const { execSync } = require('child_process');
+const fs = require('fs');
+const path = require('path');
+
+describe('Aruco Detection Script', () => {
+  const scriptPath = 'programs/01_read_Aruco_jpg_to_json.py';
+  const calibrationFile = 'data/settings/callibration_cam0.npz';
+  const screenshotsDir = 'test/data/screenShots';
+
+  test('should exist and be executable', () => {
+    expect(fs.existsSync(scriptPath)).toBe(true);
+  });
+
+  test('should have calibration file', () => {
+    expect(fs.existsSync(calibrationFile)).toBe(true);
+  });
+
+  test('should have screenshot directory', () => {
+    expect(fs.existsSync(screenshotsDir)).toBe(true);
+  });
+
+  test('should process screenshots successfully', () => {
+    const screenshots = fs.readdirSync(screenshotsDir)
+      .filter(file => file.endsWith('.jpg') || file.endsWith('.png'));
+    
+    expect(screenshots.length).toBeGreaterThan(0);
+    
+    screenshots.forEach(screenshot => {
+      const screenshotPath = path.join(screenshotsDir, screenshot);
+      const jsonPath = screenshotPath.replace(/\.(jpg|png)$/i, '_aruco_detection.json');
+      
+      // Lösche alte JSON-Datei, falls vorhanden
+      if (fs.existsSync(jsonPath)) {
+        fs.unlinkSync(jsonPath);
+      }
+      
+      // Führe das Python-Skript aus
+      const cmd = `python ${scriptPath} -i ${screenshotPath} -npz ${calibrationFile} -cameraId 0`;
+      
+      try {
+        execSync(cmd, { stdio: 'inherit' });
+        
+        // Überprüfe, ob JSON-Datei erstellt wurde
+        expect(fs.existsSync(jsonPath)).toBe(true);
+        
+        // Lese und prüfe JSON-Inhalt
+        const jsonData = JSON.parse(fs.readFileSync(jsonPath, 'utf8'));
+        expect(jsonData).toBeDefined();
+        expect(typeof jsonData).toBe('object');
+        
+      } catch (error) {
+        fail(`Failed to process ${screenshot}: ${error.message}`);
+      }
+    });
+  });
+});